In the algorithm, the gradient step performs gradient ascent using the estimated gradient \(\nabla _{\tilde{\theta }} R(\pi (\tilde{\theta }))\) . The projection step projects the updated parameters \(\theta ^{\prime i}\)  from the gradient step to the convex stability set \(\mathcal {C}(P^i,\Lambda ^i)\) , where \(P^i\)  and \(\Lambda ^i\)  are computed using \(Q_1^i\)  and \(Q_2^i\)  from the previous projection step. We choose \(\Lambda ^0 = I_{n_\phi }\) , and construct \(P^0\)  based on the method in [1]}.
